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Готові списки джерел за темами / Spatiotemporal granularitie / Статті в журналах

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Статті в журналах з теми "Spatiotemporal granularitie"

Автор: Grafiati

Опубліковано: 11 березня 2023

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1

Timko, Igor, Michael Böhlen, and Johann Gamper. "Sequenced spatiotemporal aggregation for coarse query granularities." VLDB Journal 20, no. 5 (2011): 721–41. http://dx.doi.org/10.1007/s00778-011-0247-5.

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2

Jiang, Man, Qilong Han, Haitao Zhang, and Hexiang Liu. "Spatiotemporal Data Prediction Model Based on a Multi-Layer Attention Mechanism." International Journal of Data Warehousing and Mining 19, no. 2 (2023): 1–15. http://dx.doi.org/10.4018/ijdwm.315822.

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Spatiotemporal data prediction is of great significance in the fields of smart cities and smart manufacturing. Current spatiotemporal data prediction models heavily rely on traditional spatial views or single temporal granularity, which suffer from missing knowledge, including dynamic spatial correlations, periodicity, and mutability. This paper addresses these challenges by proposing a multi-layer attention-based predictive model. The key idea of this paper is to use a multi-layer attention mechanism to model the dynamic spatial correlation of different features. Then, multi-granularity histo

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3

Wang, Pengyuan, Xiao Huang, Joseph Mango, Di Zhang, Dong Xu, and Xiang Li. "A Hybrid Population Distribution Prediction Approach Integrating LSTM and CA Models with Micro-Spatiotemporal Granularity: A Case Study of Chongming District, Shanghai." ISPRS International Journal of Geo-Information 10, no. 8 (2021): 544. http://dx.doi.org/10.3390/ijgi10080544.

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Studying population prediction under micro-spatiotemporal granularity is of great significance for modern and refined urban traffic management and emergency response to disasters. Existing population studies are mostly based on census and statistical yearbook data due to the limitation of data collecting methods. However, with the advent of techniques in this information age, new emerging data sources with fine granularity and large sample sizes have provided rich materials and unique venues for population research. This article presents a new population prediction model with micro-spatiotempo

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4

Kragh-Furbo, Mette, and Gordon Walker. "Electricity as (Big) Data: Metering, spatiotemporal granularity and value." Big Data & Society 5, no. 1 (2018): 205395171875725. http://dx.doi.org/10.1177/2053951718757254.

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Electricity is hidden within wires and networks only revealing its quantity and flow when metered. The making of its properties into data is therefore particularly important to the relations that are formed around electricity as a produced and managed phenomenon. We propose approaching all metering as a situated activity, a form of quantification work in which data is made and becomes mobile in particular spatial and temporal terms, enabling its entry into data infrastructures and schemes of evaluation and value production. We interrogate the transition from the pre-digital into the making of

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5

Kupfer, John A., Zhenlong Li, Huan Ning, and Xiao Huang. "Using Mobile Device Data to Track the Effects of the COVID-19 Pandemic on Spatiotemporal Patterns of National Park Visitation." Sustainability 13, no. 16 (2021): 9366. http://dx.doi.org/10.3390/su13169366.

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Effective quantification of visitation is important for understanding many impacts of the COVID-19 pandemic on national parks and other protected areas. In this study, we mapped and analyzed the spatiotemporal patterns of visitation for six national parks in the western U.S., taking advantage of large mobility records sampled from mobile devices and released by SafeGraph as part of their Social Distancing Metric dataset. Based on comparisons with visitation statistics released by the U.S. National Park Service, our results confirmed that mobility records from digital devices can effectively ca

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6

Ma, Jun, Yuexiong Ding, Vincent J. L. Gan, Changqing Lin, and Zhiwei Wan. "Spatiotemporal Prediction of PM2.5 Concentrations at Different Time Granularities Using IDW-BLSTM." IEEE Access 7 (2019): 107897–907. http://dx.doi.org/10.1109/access.2019.2932445.

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7

Ottaviano, Flavia, Fabing Cui, and Andy H. F. Chow. "Modeling and Data Fusion of Dynamic Highway Traffic." Transportation Research Record: Journal of the Transportation Research Board 2644, no. 1 (2017): 92–99. http://dx.doi.org/10.3141/2644-11.

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This paper presents a data fusion framework for processing and integrating data collected from heterogeneous sources on motorways to generate short-term predictions. Considering the heterogeneity in spatiotemporal granularity in data from different sources, an adaptive kernel-based smoothing method was first used to project all data onto a common space–time grid. The data were then integrated through a Kalman filter framework build based on the cell transmission model for generating short-term traffic state prediction. The algorithms were applied and tested with real traffic data collected fro

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8

Wang, Ruxin, Hongyan Wu, Yongsheng Wu, Jing Zheng, and Ye Li. "Improving influenza surveillance based on multi-granularity deep spatiotemporal neural network." Computers in Biology and Medicine 134 (July 2021): 104482. http://dx.doi.org/10.1016/j.compbiomed.2021.104482.

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9

Chen, F., C. Jing, H. Zhang, and X. Lv. "WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 493–99. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-493-2022.

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Abstract. Student behavior research can improve learning efficiency, provide decision evidences for infrastructure management. Existing campus-scale behavioral analysis work have not taken into account the students characteristics and spatiotemporal pattern. Moreover, the visualization methods are weak in wholeness, intuitiveness and interactivity perspectives. In this paper, we design a geospatial dashboard-based student behavior analysis and visualization system considering students characteristics and spatiotemporal pattern. This system includes four components: user monitoring, data mining

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10

Jian, Yang, Jinhong Li, Lu Wei, Lei Gao, and Fuqi Mao. "Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting." Journal of Advanced Transportation 2022 (April 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/4260244.

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As a typical spatiotemporal problem, there are three main challenges in traffic forecasting. First, the road network is a nonregular topology, and it is difficult to extract complex spatial dependence accurately. Second, there are short- and long-term dependencies between traffic dates. Third, there are many other factors besides the influence of spatiotemporal dependence, such as semantic characteristics. To address these issues, we propose a spatiotemporal DeepWalk gated recurrent unit model (ST-DWGRU), a deep learning framework that fuses spatial, temporal, and semantic features for traffic

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11

Zhou, Kaichun, Zongshun Tian, and Yuanwei Yang. "Periodic Pattern Detection Algorithms for Personal Trajectory Data Based on Spatiotemporal Multi-Granularity." IEEE Access 7 (2019): 99683–93. http://dx.doi.org/10.1109/access.2019.2930619.

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12

Cardim, Luiz Henrique Anjos, and Nádia Puchalski Kozievitch. "Rastreia Saúde: A Spatiotemporal Disease Tracking System through Open Unstructured Data and GIS." Revista Brasileira de Cartografia 73, no. 4 (2021): 999–1016. http://dx.doi.org/10.14393/rbcv73n4-59881.

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Automated disease tracking has become an increasingly important tool today. This article describes the prototype of a disease tracking system for the Brazilian territory, preliminarily tested at the state level, in Paraná, and at the municipal level, in Curitiba. This study aims to extract and present relevant information in the health segment from unstructured data, extracted from news portals. The system generates data that allows analysis at different levels of granularity, from small municipalities to the national level. The results of the study shows the viability of the system and allows

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13

Jiang, Feifeng, Jun Ma, and Zheng Li. "Pedestrian volume prediction with high spatiotemporal granularity in urban areas by the enhanced learning model." Sustainable Cities and Society 79 (April 2022): 103653. http://dx.doi.org/10.1016/j.scs.2021.103653.

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14

Xiao, Chuanliang, Lei Sun, and Ming Ding. "Multiple Spatiotemporal Characteristics-Based Zonal Voltage Control for High Penetrated PVs in Active Distribution Networks." Energies 13, no. 1 (2020): 249. http://dx.doi.org/10.3390/en13010249.

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The penetration of photovoltaic (PV) outputs brings great challenges to optimal operation of active distribution networks (ADNs), especially leading to more serious overvoltage problems. This study proposes a zonal voltage control scheme based on multiple spatiotemporal characteristics for highly penetrated PVs in ADNs. In the spatial domain, a community detection algorithm using a reactive/ active power quality function was introduced to partition an ADN into sub-networks. In the time domain, short-term zonal scheduling (SZS) with 1 h granularity was drawn up based on a cluster. The objective

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15

Jing, Changfeng, Shasha Guo, Hongyang Zhang, Xinxin Lv, and Dongliang Wang. "SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus." ISPRS International Journal of Geo-Information 11, no. 3 (2022): 194. http://dx.doi.org/10.3390/ijgi11030194.

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To achieve Sustainable Development Goal 7 (SDG7), it is essential to detect the spatiotemporal patterns of electricity consumption, particularly the spatiotemporal heterogeneity of consumers. This is also crucial for rational energy planning and management. However, studies investigating heterogeneous users are lacking. Moreover, existing works focuses on mathematic models to identify and predict electricity consumption. Additionally, owing to the complex non-linear interrelationships, interactive visualizations are more effective in detecting patterns. Therefore, by combining geospatial dashb

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16

Shen, Nuozhou, Haiping Zhang, Haoran Wang, Xuanhong Zhou, Lei Zhou, and Guo’An Tang. "Toward multi-granularity spatiotemporal simulation modeling of crowd movement for dynamic assessment of tourist carrying capacity." GIScience & Remote Sensing 59, no. 1 (2022): 1857–81. http://dx.doi.org/10.1080/15481603.2022.2139450.

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17

Li, Mingxiao, Song Gao, Feng Lu, Huan Tong, and Hengcai Zhang. "Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data." International Journal of Environmental Research and Public Health 16, no. 22 (2019): 4522. http://dx.doi.org/10.3390/ijerph16224522.

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The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. With the advantages of massive users, wide spatial coverage and passive acquisition capability, mobile phone data have become an emerging data source for compiling exposure estimates. However, compared with air pollution monitoring data, the temporal granularity of mobile phone data is not high enough, which limi

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18

Ramadoss, Balakrishnan, and Kannan Rajkumar. "Modelling and Querying the Expressive Semantics of Dance Videos." Journal of Information & Knowledge Management 05, no. 03 (2006): 193–210. http://dx.doi.org/10.1142/s0219649206001463.

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Dance videos are interesting and semantics-intensive. At the same time, they are complex type of videos, when compared to all other types such as sports, news and movie videos. In fact, dance video is the one which is less explored by the researchers across the globe. This paper presents a dance video data model to represent the semantics of the dance videos with different granularity levels, identified by the components of the accompanying song. Secondly, the paper proposes a multi-level index structure to efficiently handle containment, temporal, spatial and spatiotemporal query types. Moreo

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19

Näpflin, Kathrin, Emily A. O’Connor, Lutz Becks, et al. "Genomics of host-pathogen interactions: challenges and opportunities across ecological and spatiotemporal scales." PeerJ 7 (November 5, 2019): e8013. http://dx.doi.org/10.7717/peerj.8013.

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Evolutionary genomics has recently entered a new era in the study of host-pathogen interactions. A variety of novel genomic techniques has transformed the identification, detection and classification of both hosts and pathogens, allowing a greater resolution that helps decipher their underlying dynamics and provides novel insights into their environmental context. Nevertheless, many challenges to a general understanding of host-pathogen interactions remain, in particular in the synthesis and integration of concepts and findings across a variety of systems and different spatiotemporal and ecolo

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20

Zhao, Jun, Yang Liu, Witold Pedrycz, and Wei Wang. "Spatiotemporal Prediction for Energy System of Steel Industry by Generalized Tensor Granularity Based Evolving Type-2 Fuzzy Neural Network." IEEE Transactions on Industrial Informatics 17, no. 12 (2021): 7933–45. http://dx.doi.org/10.1109/tii.2021.3062036.

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21

Park, Jinwoo, and Daniel W. Goldberg. "A Review of Recent Spatial Accessibility Studies That Benefitted from Advanced Geospatial Information: Multimodal Transportation and Spatiotemporal Disaggregation." ISPRS International Journal of Geo-Information 10, no. 8 (2021): 532. http://dx.doi.org/10.3390/ijgi10080532.

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Spatial accessibility provides significant policy implications, describing the spatial disparity of access and supporting the decision-making process for placing additional infrastructure at adequate locations. Several previous reviews have covered spatial accessibility literature, focusing on empirical findings, distance decay functions, and threshold travel times. However, researchers have underexamined how spatial accessibility studies benefitted from the recently enhanced availability of dynamic variables, such as various travel times via different transportation modes and the finer tempor

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22

Uhl, Johannes H., Stefan Leyk, Caitlin M. McShane, Anna E. Braswell, Dylan S. Connor, and Deborah Balk. "Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States." Earth System Science Data 13, no. 1 (2021): 119–53. http://dx.doi.org/10.5194/essd-13-119-2021.

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Abstract. The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth's surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250 m spa

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23

Jiang, Xuexia, Tadamoto Isogai, Joseph Chi, and Gaudenz Danuser. "Fine-grained, nonlinear registration of live cell movies reveals spatiotemporal organization of diffuse molecular processes." PLOS Computational Biology 18, no. 12 (2022): e1009667. http://dx.doi.org/10.1371/journal.pcbi.1009667.

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We present an application of nonlinear image registration to align in microscopy time lapse sequences for every frame the cell outline and interior with the outline and interior of the same cell in a reference frame. The registration relies on a subcellular fiducial marker, a cell motion mask, and a topological regularization that enforces diffeomorphism on the registration without significant loss of granularity. This allows spatiotemporal analysis of extremely noisy and diffuse molecular processes across the entire cell. We validate the registration method for different fiducial markers by m

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24

Rusňák, Tomáš, Andrej Halabuk, Ľuboš Halada, Hubert Hilbert, and Katarína Gerhátová. "Detection of Invasive Black Locust (Robinia pseudoacacia) in Small Woody Features Using Spatiotemporal Compositing of Sentinel-2 Data." Remote Sensing 14, no. 4 (2022): 971. http://dx.doi.org/10.3390/rs14040971.

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Recognition of invasive species and their distribution is key for managing and protecting native species within both natural and man-made ecosystems. Small woody features (SWF) represent fragmented patches or narrow linear tree features that are of high importance in intensively utilized agricultural landscapes. Simultaneously, they frequently serve as expansion pathways for invasive species such as black locust. In this study, Sentinel-2 products, combined with spatiotemporal compositing approaches, are used to address the challenge of broad area black locust mapping at a high granularity. Th

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25

Li, Xiantong, Hua Wang, Pengcheng Sun, and Hongquan Zu. "Spatiotemporal Features—Extracted Travel Time Prediction Leveraging Deep-Learning-Enabled Graph Convolutional Neural Network Model." Sustainability 13, no. 3 (2021): 1253. http://dx.doi.org/10.3390/su13031253.

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Travel time prediction is one of the most important parameters to forecast network-wide traffic conditions. Travelers can access traffic roadway networks and arrive in their destinations at the lowest costs guided by accurate travel time estimation on alternative routes. In this study, we propose a long short-term memory (LSTM)-based deep learning model, deep learning on spatiotemporal features with Convolution Neural Network (DLSF-CNN), to extract the spatial–temporal correlation of travel time on different routes to accurately predict route travel time. Specifically, this model utilizes netw

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26

Chen, Junzhou, Jiancheng Wang, Jiajun Pu, and Ronghui Zhang. "A Three-Stage Anomaly Detection Framework for Traffic Videos." Journal of Advanced Transportation 2022 (July 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/9463559.

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As reported by the United Nations in 2021, road accidents cause 1.3 million deaths and 50 million injuries worldwide each year. Detecting traffic anomalies timely and taking immediate emergency response and rescue measures are essential to reduce casualties, economic losses, and traffic congestion. This paper proposed a three-stage method for video-based traffic anomaly detection. In the first stage, the ViVit network is employed as a feature extractor to capture the spatiotemporal features from the input video. In the second stage, the class and patch tokens are fed separately to the segment-

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27

Bauer, Cici, Kehe Zhang, Wenjun Li, et al. "Small Area Forecasting of Opioid-Related Mortality: Bayesian Spatiotemporal Dynamic Modeling Approach." JMIR Public Health and Surveillance 9 (February 10, 2023): e41450. http://dx.doi.org/10.2196/41450.

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Background Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation. Objective The overarching objective of our study w

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28

Li, Kenan, Sandrah P. Eckel, Erika Garcia, Zhanghua Chen, John P. Wilson, and Frank D. Gilliland. "Geographic Variations in Human Mobility Patterns during the First Six Months of the COVID-19 Pandemic in California." Applied Sciences 13, no. 4 (2023): 2440. http://dx.doi.org/10.3390/app13042440.

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Human mobility influenced the spread of the COVID-19 virus, as revealed by the high spatiotemporal granularity location service data gathered from smart devices. We conducted time series clustering analysis to delineate the relationships between human mobility patterns (HMPs) and their social determinants in California (CA) using aggregated smart device tracking data from SafeGraph. We first identified four types of temporal patterns for five human mobility indicator changes by applying dynamic-time-warping self-organizing map clustering methods. We then performed an analysis of variance and l

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29

Li, Sijia, Chao Wu, Yu Lin, Zhengyang Li, and Qingyun Du. "Urban Morphology Promotes Urban Vibrancy from the Spatiotemporal and Synergetic Perspectives: A Case Study Using Multisource Data in Shenzhen, China." Sustainability 12, no. 12 (2020): 4829. http://dx.doi.org/10.3390/su12124829.

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Urban vibrancy is the key and the foundation for monitoring the status of urban spatial development, assisting in data-driven urban development planning and realizing sustainable urban development. Based on a dataset of multisource geographical big data, the understanding and analysis of urban vibrancy can be deepened with fine granularity. The working framework in this study focuses on the comprehensive perspective of urban morphology, which is decomposed into two dimensions (formality and functionality) and four elements (road, block, building, point of interest). The geographically and temp

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30

Zuo, Chenyu, Linfang Ding, and Liqiu Meng. "Visual Analytics for Regional Economic Environment Factors Based on a Dashboard Design." Proceedings of the ICA 2 (July 10, 2019): 1–8. http://dx.doi.org/10.5194/ica-proc-2-158-2019.

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<p><strong>Abstract.</strong> Economic environment is vital for commercial investment, city planning and company strategy planning in urban areas. Mastering the economical trend may help the entrepreneurs, government officers and individuals in their decision-making process. In this study, we explore multiple geo-economic datasets using visual analytics methods for understanding the economic environment. More specifically, we user time-series Gross Domestic Product (GDP) data as an economic indicator of economic development and land use data to support the spatial analysis at

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31

Victor, Jonathan D., and Mary M. Conte. "Evoked potential and psychophysical analysis of Fourier and non-Fourier motion mechanisms." Visual Neuroscience 9, no. 2 (1992): 105–23. http://dx.doi.org/10.1017/s0952523800009573.

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AbstractSome visual stimuli produce a strong percept of motion, even though they fail to excite motion detectors based on Fourier energy or cross correlation. Models which suffice to explain the motion percept in these non-Fourier motion (NFM) stimuli include linear spatiotemporal filtering, followed by rectification, followed by standard motion analysis (Chubb & Sperling 1988). We used the human “motion-onset” evoked potential, which has been assigned to area 17 on the basis of work in the macaque (van Dijk et al., 1986; van Dijk & Spekreijse, 1989), to investigate the neural substrat

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32

Si, Yutian, Liyan Xu, Xiao Peng, and Aihan Liu. "Comparative Diagnosis of the Urban Noise Problem from Infrastructural and Social Sensing Approaches: A Case Study in Ningbo, China." International Journal of Environmental Research and Public Health 19, no. 5 (2022): 2809. http://dx.doi.org/10.3390/ijerph19052809.

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Urban noise causes a variety of health problems, and its prevention and control have thus become an important research topic in urban governance. Although existing literature is fairly comprehensive in revealing the physical noise patterns, it lacks the concern of people’s perceived seriousness, especially at the macroscopic, i.e., citywide scale. In this paper, we borrow from the “exposure-perception-behavior” theory in environmental psychology, and propose an analytical framework for diagnosing the urban noise problem that integrates the Infrastructural and Social Sensing perspectives. Utili

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33

Minelli, Annalisa, Iwan Le Berre, Ingrid Peuziat, and Mathias Rouan. "Reconstruction of Marine Traffic from Sémaphore Data: A Python-GIS Procedure to Build Synthetic Navigation Routes and Analyze Their Temporal Variation." Journal of Marine Science and Engineering 9, no. 3 (2021): 294. http://dx.doi.org/10.3390/jmse9030294.

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Originally designed as a mode of telecommunication, the network of French sémaphore is now dedicated to the continuous monitoring and recording of marine traffic along the entire French coast. Although the observation data collected by sémaphores cover 7/7 days and 24/24 h and could provide precious information regarding marine traffic, they remain underexploited. Indeed, these data concern all types of traffic, including leisure boating and smaller craft that are not usually recorded by the most common means of observation, such as AIS, radar and satellite. Based on sémaphore data, traffic pr

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34

Raveh, Barak, Liping Sun, Kate L. White, et al. "Bayesian metamodeling of complex biological systems across varying representations." Proceedings of the National Academy of Sciences 118, no. 35 (2021): e2104559118. http://dx.doi.org/10.1073/pnas.2104559118.

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Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on

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35

Jiang, Haonan, Timo Balz, Francesca Cigna, and Deodato Tapete. "Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data." Remote Sensing 13, no. 7 (2021): 1256. http://dx.doi.org/10.3390/rs13071256.

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Wuhan is an important city in central China, with a rapid development that has led to increasingly serious land subsidence over the last decades. Most of the existing Interferometric Synthetic Aperture Radar (InSAR) subsidence monitoring studies in Wuhan are either short-term investigations—and thus can only detect this process within limited time periods—or combinations of different Synthetic Aperture Radar (SAR) datasets with temporal gaps in between. To overcome these constraints, we exploited nearly 300 high-resolution COSMO-SkyMed StripMap HIMAGE scenes acquired between 2012 and 2019 to m

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Jones, Rodney P. "Excess Winter Mortality (EWM) as a Dynamic Forensic Tool: Where, When, Which Conditions, Gender, Ethnicity and Age." International Journal of Environmental Research and Public Health 18, no. 4 (2021): 2161. http://dx.doi.org/10.3390/ijerph18042161.

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To investigate the dynamic issues behind intra- and international variation in EWM (Excess Winter Mortality) using a rolling monthly EWM calculation. This is used to reveal seasonal changes in the EWM calculation and is especially relevant nearer to the equator where EWM does not reach a peak at the same time each year. In addition to latitude country specific factors determine EWM. Females generally show higher EWM. Differences between the genders are highly significant and seem to vary according to the mix of variables active each winter. The EWM for respiratory conditions in England and Wal

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Gouripeddi, Ram, Andrew Miller, Karen Eilbeck, Katherine Sward, and Julio C. Facelli. "3399 Systematically Integrating Microbiomes and Exposomes for Translational Research." Journal of Clinical and Translational Science 3, s1 (2019): 29–30. http://dx.doi.org/10.1017/cts.2019.71.

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OBJECTIVES/SPECIFIC AIMS: Characterize microbiome metadata describing specimens collected, genomic pipelines and microbiome results, and incorporate them into a data integration platform for enabling harmonization, integration and assimilation of microbial genomics with exposures as spatiotemporal events. METHODS/STUDY POPULATION: We followed similar methods utilized in previous efforts in charactering and developing metadata models for describing microbiome metadata. Due to the heterogeneity in microbiome and exposome data, we aligned them along a conceptual representation of different data u

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Ji, Fang, Linfeng Fan, Xingxing Kuang, et al. "How does soil water content influence permafrost evolution on the Qinghai-Tibet plateau under climate warming?" Environmental Research Letters, May 4, 2022. http://dx.doi.org/10.1088/1748-9326/ac6c9a.

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Abstract The active layer thickness (ALT) in permafrost regions regulates hydrological cycles, water sustainability, and ecosystem functions in the cryosphere and is extremely sensitive to climate change. Previous studies often focused on the impacts of rising temperature on the ALT, while the roles of soil water content and soil granularity have rarely been investigated. Here, we incorporate alterations of soil water contents in soil thermal properties across various soil granularities and assess spatiotemporal ALT dynamics on the Qinghai-Tibet Plateau (QTP). The regional average ALT on the Q

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39

Zha, Cheng, Weidong Min, Qing Han, Xin Xiong, Qi Wang, and Qian Liu. "Multiple Granularity Spatiotemporal Network for Sea Surface Temperature Prediction." IEEE Geoscience and Remote Sensing Letters, 2022, 1. http://dx.doi.org/10.1109/lgrs.2022.3167744.

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Zhou, Zhengyang, Yang Wang, Xike Xie, Lianliang Chen, and Chaochao Zhu. "Foresee Urban Sparse Traffic Accidents: A Spatiotemporal Multi-Granularity Perspective." IEEE Transactions on Knowledge and Data Engineering, 2020, 1. http://dx.doi.org/10.1109/tkde.2020.3034312.

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41

Zhao, Shuai, Daxing Zhao, Ruiqiang Liu, Zhen Xia, Bo Cheng, and Junliang Chen. "GMAT-DU: Traffic Anomaly Prediction With Fine Spatiotemporal Granularity in Sparse Data." IEEE Transactions on Intelligent Transportation Systems, 2023, 1–15. http://dx.doi.org/10.1109/tits.2023.3249409.

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Cheng, Zhifeng, Jianghao Wang, Kaixin Zhu, Yong Ge, and Chenghu Zhou. "Evaluating spatial statistical and machine learning models in urban dynamic population mapping." Transactions in Urban Data, Science, and Technology, August 5, 2022, 275412312211141. http://dx.doi.org/10.1177/27541231221114169.

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Understanding population dynamics at fine spatiotemporal granularities are valuable to human-centered studies. With the increasing availability of high-frequency human digital footprint data, the past decades have witnessed numerous efforts in mapping populations at fine spatiotemporal scales. However, such research still lacks a unified standard in modeling strategy and auxiliary data selection, especially a systematic comparison between newly developed machine learning techniques and traditional spatial statistical methods under different covariates provisions. Here, we compared two spatial

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43

Liu, Kai, Zhiju Chen, Toshiyuki Yamamoto, and Liheng Tuo. "Exploring the Impact of Spatiotemporal Granularity on the Demand Prediction of Dynamic Ride-Hailing." IEEE Transactions on Intelligent Transportation Systems, 2022, 1–11. http://dx.doi.org/10.1109/tits.2022.3216016.

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Lyu, Fangzheng, Shaohua Wang, Su Yeon Han, Charlie Catlett, and Shaowen Wang. "An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data." Urban Informatics 1, no. 1 (2022). http://dx.doi.org/10.1007/s44212-022-00002-4.

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AbstractDue to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) fram

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Xiao, Jiang, Huichuwu Li, Minrui Wu, Hai Jin, M. Jamal Deen, and Jiannong Cao. "A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open Issues." ACM Computing Surveys, April 19, 2022. http://dx.doi.org/10.1145/3530682.

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In the last decade, many studies have significantly pushed the limits of wireless device-free human sensing (WDHS) technology and facilitated various applications, ranging from activity identification to vital sign monitoring. This survey presents a novel taxonomy that classifies the state-of-the-art WDHS systems into eleven categories according to their sensing task type and motion granularity . In particular, existing WDHS systems involve three primary sensing task types. The first type, behavior recognition , is a classification problem of recognizing predefined meaningful behaviors. The se

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Hohl, Alexander, Wenwu Tang, Irene Casas, Xun Shi, and Eric Delmelle. "Detecting space–time patterns of disease risk under dynamic background population." Journal of Geographical Systems, April 20, 2022. http://dx.doi.org/10.1007/s10109-022-00377-7.

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AbstractWe are able to collect vast quantities of spatiotemporal data due to recent technological advances. Exploratory space–time data analysis approaches can facilitate the detection of patterns and formation of hypotheses about their driving processes. However, geographic patterns of social phenomena like crime or disease are driven by the underlying population. This research aims for incorporating temporal population dynamics into spatial analysis, a key omission of previous methods. As population data are becoming available at finer spatial and temporal granularity, we are increasingly ab

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Lee, Jeong-Jun, Wenrui Zhang, Yuan Xie, and Peng Li. "SaARSP: An Architecture for Systolic-Array Acceleration of Recurrent Spiking Neural Networks." ACM Journal on Emerging Technologies in Computing Systems, June 27, 2022. http://dx.doi.org/10.1145/3510854.

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Spiking neural networks (SNNs) are brain-inspired event-driven models of computation with promising ultra-low energy dissipation. Rich network dynamics emergent in recurrent spiking neural networks (R-SNNs) can form temporally-based memory, offering great potential in processing complex spatiotemporal data. However, recurrence in network connectivity produces tightly coupled data dependency in both space and time, rendering hardware acceleration of R-SNNs challenging. We present the first work to exploit spatiotemporal parallelisms to accelerate the R-SNN based inference on systolic arrays usi

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Xiao, Xin, Chaoyang Fang, Hui Lin, Li Liu, Ya Tian, and Qinghua He. "Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China." Computational Urban Science 2, no. 1 (2022). http://dx.doi.org/10.1007/s43762-021-00030-x.

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AbstractIn the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was

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Dong, Lei, Xiaohui Yuan, Meng Li, Carlo Ratti, and Yu Liu. "A gridded establishment dataset as a proxy for economic activity in China." Scientific Data 8, no. 1 (2021). http://dx.doi.org/10.1038/s41597-020-00792-9.

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AbstractMeasuring the geographical distribution of economic activity plays a key role in scientific research and policymaking. However, previous studies and data on economic activity either have a coarse spatial resolution or cover a limited time span, and the high-resolution characteristics of socioeconomic dynamics are largely unknown. Here, we construct a dataset on the economic activity of mainland China, the gridded establishment dataset (GED), which measures the volume of establishments at a 0.01° latitude by 0.01° longitude scale. Specifically, our dataset captures the geographically ba

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Dabaghian, Yuri. "From Topological Analyses to Functional Modeling: The Case of Hippocampus." Frontiers in Computational Neuroscience 14 (January 11, 2021). http://dx.doi.org/10.3389/fncom.2020.593166.

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Topological data analyses are widely used for describing and conceptualizing large volumes of neurobiological data, e.g., for quantifying spiking outputs of large neuronal ensembles and thus understanding the functions of the corresponding networks. Below we discuss an approach in which convergent topological analyses produce insights into how information may be processed in mammalian hippocampus—a brain part that plays a key role in learning and memory. The resulting functional model provides a unifying framework for integrating spiking data at different timescales and following the course of

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